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                <pre class="line-numbers language-python" data-language="python"><code class="language-python">import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Activation
from keras.optimizers import RMSprop<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">Using TensorFlow backend.<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<h4 id="导入mnist数据集">导入mnist数据集</h4>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">(X_train,y_train),(X_test,y_test) &#x3D; mnist.load_data()<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<h4 id="数据预处理">数据预处理</h4>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">X_train &#x3D; X_train.reshape(X_train.shape[0],-1)&#x2F;255 # 标准化
X_test &#x3D; X_test.reshape(X_test.shape[0],-1)&#x2F;255 # 标准化<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">y_train &#x3D; np_utils.to_categorical(y_train)
y_test &#x3D; np_utils.to_categorical(y_test)<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
<h4 id="建立一种神经网络方法">建立一种神经网络方法</h4>
<p>在Sequential添加参数，它会从上向下运行，我们添加第一层输入数据为数据集的尺寸28*28，输出数据大小压缩为32，这里的激活函数用’relu‘。然后添加第二层，这里第二层的输入数据，就是第一层的输出数据，默认为32。输出数据为10，分别代表数字一到十。</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">model &#x3D; Sequential([
    Dense(32,input_dim&#x3D;28*28),
    Activation(&#39;relu&#39;),
    Dense(10),
    Activation(&#39;softmax&#39;)])<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<h4 id="建立并定义优化器（optimizer）">建立并定义优化器（optimizer）</h4>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">rmsprop &#x3D; RMSprop(lr&#x3D;0.001,rho&#x3D;0.9,epsilon&#x3D;1e-08,decay&#x3D;0.0) #设置learning rate<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<h4 id="RMSprop-optimizer参数">RMSprop optimizer参数</h4>
<ul>
<li>learning rate：浮点值，或 是 的计划，或不采用参数并返回要使用的实际值的可调用。学习率。默认值为 0.001。Tensortf.keras.optimizers.schedules.LearningRateSchedule</li>
<li>rho: 历史/未来梯度的折扣系数。默认值为 0.9。</li>
<li>momentum(动量):标量或标量。默认为0.0</li>
<li>epsilon: 用于数值稳定性的小常数。此 epsilon 是 Kingma 和 Ba 纸张中的 “epsilon 帽子”（在第 2.1 节前的公式中），而不是纸张算法 1 中的 epsilon。默认值为 1e-7。</li>
<li>centered: 布尔。如果 ，梯度由渐变的估计方差进行规范化;如果 ，则 为 梯度的预计方差进行规范化。如果错误，由非中心的第二个时刻。将其设置为可能有助于训练，但在计算和内存方面略贵。默认值为 。TrueTrueFalse</li>
<li>name: 应用渐变时创建的操作的可选名称前缀。默认值为 。“RMSprop”</li>
<li>**kwargs: 关键字参数。允许为 或 之一。 （浮动）按规范剪辑渐变; （浮动）按值剪辑渐变。“clipnorm”“clipvalue”“clipnorm”“clipvalue”</li>
</ul>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">model.compile(optimizer&#x3D;rmsprop,                   #这里用我们设置好的RMSprop优化器，也可以使用默认的‘rmsprop’
             loss&#x3D;&#39;categorical_crossentropy&#39;,      #这里用交叉熵来计算损失值
             metrics&#x3D;[&#39;accuracy&#39;],                 #评估标准
             )<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span></span></code></pre>
<h4 id="开始训练">开始训练</h4>
<p>fit方法：在训练上拟合（fit）模型</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">print(&#39;Training--------&#39;)
model.fit(X_train,y_train, epochs&#x3D;2,batch_size&#x3D;32) #这里的epochs在以前的版本为nb_epoch,训练epochs为两次，批量大小为32。<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">Training--------
Epoch 1&#x2F;2
60000&#x2F;60000 [&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;] - 3s 52us&#x2F;step - loss: 0.1112 - accuracy: 0.9688
Epoch 2&#x2F;2
60000&#x2F;60000 [&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;&#x3D;] - 3s 54us&#x2F;step - loss: 0.1046 - accuracy: 0.9703





&lt;keras.callbacks.callbacks.History at 0x1cca0ee0c48&gt;<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

                
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